5 Best Practices for Big Data Project Management

From product marketing to the healthcare sector, big data analytics is helping transform massive collection of data into insightful information that have continued to prove useful in the decision-making process of organizations worldwide. This technology breaks down large amount of data and identifies patterns (insights) which businesses are now using as their customer insight to bridge the gaps between consumer behavior and product development.

Here are 5 best practices that you can follow to effectively manage big data.

Define clear objectives

Before embarking on any big data project, it is necessary to state the problem you intend to solve with the data. For example, you can use big data to track customer preferences and provide them with highly tailored recommendations from your product or service range. A clear knowledge of what you’re looking to achieve would serve as a guide which will enable you to find the suitable big data analytic that can add value to your business and provide you with the desired insights you need to create those solutions (You can’t hit a target you cannot see.). These objectives could vary depending on the type importance of big data in higher education of services or product which your organization offers.

Build organization-wide support

Once a clear objective has been established for the implementation of big data, it is imperative to seek the support of all business stakeholders and key players that will be involved in the project. This cooperation and support would ensure that the teams and employees who will eventually use big data analytics insights can contribute effectively to the project and overall business performance. Without this support from stakeholder, it will be difficult to get the funding needed to adopt this technology and more so, considering the reluctance businesses have to changes to their normal way of planning and decision-making. It is imperative that everyone is on the same page before committing to use big data analytics.

Adopting big data would also require training of employees in order learn the architecture of these data and to understand how to effectively use the insights which the technology creates.

Analyze only useful data

Massive data could be overwhelming if you are unable to discover the patterns and translate them into “actionable insights”. That said, only relevant and actionable data that adds value and brings you closer to the solution you intend to provide are of importance. Any data that cannot be put into sufficient use shouldn’t be analyzed. Big data can only help streamline those data which you have considered viable for your business; every other data is just a byproduct. There is no need for taking up storage space for data that you are likely not going to use.

This is the core reason for setting a clear objective before collecting any type of data. For example a company that wants to go into manufacturing of boats would consider data from areas that have access to water (rivers, lakes, seas, etc.) in order to have an idea of the demand.

Make the data accessible and secure to those that need it

The insights generated from mining big data should be made available to a cross-section of departments and teams such as finance, marketing, public relation, etc. This access to data by different department enables them to effective utilize and map-out actionable strategies and decisions. The data manager is responsible for making sure that all organizational data is made available to those that need it.

To ensure a secure and seamless synchronization of data, the organization can make use of cloud base storage, remote database administrator, cyber encryption and other data management tools to grant access to only permitted employees across the cross-section of the organization that need it especially if it’s a big company with different branches across the world. This practice helps protect your data from hackers and other unwanted individuals who could be a threat to your organization.

Start small and scale up gradually

When it comes to managing big data, it is advisable to begin implementing it on small projects and gradually scaling up. This helps to minimize risk and build the competence of your team in handling data. It is always good practice to test the waters before adopting big data on a large scale, this helps maintain consistency of data as the business begins to expand. Businesses must overcome the tendency of adopting the new technology of big data expecting immediate transformation and growth. You can go as far as considering the use of prototypes before jumping into big data.

Remember, the aim is to create actionable insights not just collecting massive amount of data so start interaction by analyzing small data and build the momentum and better understanding while scaling up.

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